Nigel John Burroughs

The emergence of automated microscopy has enabled the collection of large datasets of rare biological events, accelerating discovery in biology. However, most existing methods cannot be readily integrated into microscopy setups or are limited to a single microscope type. We present CelFDrive, a software package that automatically detects rare events of interest and automates high-resolution three-dimensional imaging of target cells by integrating deep learning-assisted cell classification of auxiliary low-magnification fluorescence images. We show that CelFDrive reduced the mean time required to nominate a mitotic prophase candidate for high-resolution light-sheet imaging by over 30-fold relative to manual expert selection. This approach can be used to define the order of kinetochore assembly upon nuclear envelope breakdown. The trained CelFDrive detector demonstrated transferability by retaining mitotic cell detection capability in an independently generated dataset using a different cell line, DNA marker and imaging setup. As datasets grow and models improve, CelFDrive offers a clear path toward intelligent imaging systems that not only accelerate data collection, but also fundamentally enhance how biological questions are explored.

BioStudies – one package for all the data supporting a study

Cancer therapy must balance between two competing objectives - treatment efficacy against the tumour and the risk of treatment related severe adverse events, including patient death. While optimal control theory (OCT) provides a principled framework for designing dosing protocols, most existing formulations rely on optimising heuristic cost functionals that lack direct clinical interpretability. In clinical practice treatment efficacy and patient tolerability are primarily assessed through survival metrics and adverse event rates. Here we introduce the Continuous Lifetime Payoff (CLP), a novel OCT objective functional that directly links treatment decisions to patient survival. It explicitly incorporates tumour dynamics, tumour eradication, and patient mortality from tumour progression, drug-related toxicity and age. Mortality is modelled as a Poisson processes, a framework that enables the patient’s expected lifetime to be computed. The model and objective are biologically interpretable and can in principle be calibrated using clinical data. We demonstrate the approach for a logistic tumour growth model, illustrating the trade-offs between tumour control and toxicity. We fit age-related mortality from national life tables and demonstrate parameter inferability from overall survival curves using simulated data. The CLP offers a clinically grounded OCT objective and provides a foundation for data-driven optimisation of chemotherapy regimens.

Mitosis is a complex self-organising process that achieves high fidelity separation of duplicated chromosomes into two daughter cells through capture and alignment of chromosomes to the spindle mid-plane. Chromosome movements are driven by kinetochores (KTs), multi-protein machines that attach chromosomes to microtubules (MTs), and through those attachments both control and generate directional forces. Using lattice light sheet microscopy imaging and automated near-complete tracking of kinetochores at fine spatio-temporal resolution, we produce a detailed atlas of kinetochore metaphase-anaphase dynamics in untransformed human cells (RPE1). Such data allows dynamic models to be reverse engineered and biological hypotheses to be addressed. We determined the support from this dataset for 17 models of metaphase dynamics using Bayesian inference, demonstrating (1) substantial sister asymmetry that generates transverse organisation of the metaphase plate (MPP), (2) substantial spatial organisation of KT dynamic properties within the MPP, and (3) significant time dependence of the K-fiber mechanical parameters whereby K-fiber forces tune over the last 5 mins of metaphase towards a set point, referred to as the anaphase ready state. These spatio-temporal trends are robust to perturbation of the spindle assembly pathway (nocodazole washout treatment), suggesting that the underlying processes generating kinetochore heterogeneity are intrinsic to mitosis and possibly play a role in ensuring high-fidelity segregation.

We propose a life-expectancy pay-off function (LEP) for determining optimal cancer treatment within a control theory framework. The LEP averages life expectancy over all future outcomes, outcomes that are determined by key events during therapy such as tumour elimination (cure) and patient death (including treatment related mortality). We analyse this optimisation problem for tumours treated with chemotherapy using tumour growth models based on ordinary differential equations. To incorporate tumour elimination we draw on branching processes to compute the probability distribution of tumour population extinction. To demonstrate the approach, we apply the LEP framework to simplified one-compartment models of tumour growth that include three possible outcomes: cure, relapse, or death during treatment. Using Pontryagin’s maximum principle (PMP) we show that the best treatment strategies fall into three categories: (i) continuous treatment at the maximum tolerated dose (MTD), (ii) no treatment, or (iii) treat-and-stop therapy, where the drug is given at the MTD and then halted before the treatment (time) horizon. Optimal treatment strategies are independent of the time horizon unless the time horizon is too short to accommodate the most effective (treat-and-stop) therapy. For sufficiently long horizons, the optimal solution is either no treatment (when treatment yields no benefit) or treat-and-stop. Patients, thus, split into an untreatable class and a treatable class, with patient demographics, tumour size, tumour response, and drug toxicity determining whether a patient benefits from treatment. The LEP is in principle parametrisable from data, requiring estimation of the rates of each event and the associated life expectancy under that event. This makes the approach suitable for personalising cancer therapy based on tumour characteristics and patient-specific risk profiles.

The Spindle Assembly Checkpoint (SAC) delays anaphase onset until all kinetochores are stably attached to microtubules, thus promoting error-free chromosome segregation. Multiple molecular events are implicated in SAC silencing, including removal of phospho-marks, protein (un)binding, and structural reorganisation of the kinetochore – but we currently lack a quantitative map of how these events unfold through time. Here, we use the levels of the checkpoint protein MAD2 to create a pseudo-timeline of SAC silencing at single kinetochores. We demonstrate how silencing proceeds through an ordered series of molecular events where MAD2-Spindly unbinds first and then the KNL1 catalytic platform disassembles, with a pool of active MPS1 retained. Coincidently, the NDC80 ensemble reconfigures in response to high microtubule occupancy. Kinetochores next switch into a mature attachment state that then undergoes gradual further stabilisation through NDC80 tail dephosphorylation. By preventing biorientation, we also define otherwise hidden kinetochore states involved in error correction cycles. This includes a “poised” state which we propose allows for error correction and rapid reactivation of the SAC. These results provide a critical temporal framework for understanding the mechanisms of SAC silencing and error correction at single human kinetochores.

Human reproduction is inherently inefficient ¹ and 1 in 6 people worldwide suffer infertility. In vitro fertilisation (IVF) can help some couples conceive, but only ~30% of cycles are successful. One factor affecting IVF efficacy is mitotic-origin (mosaic) aneuploidy in which embryos contain a mixture of cells with different numbers of chromosomes ² . We previously showed that chromosome segregation error phenotypes are frequent in the first mitotic division of the human embryo ³ . However, the cause of these errors and impact on daughter cell karyotype is unknown. Here, using live chromosome imaging and next generation sequencing we show that activation of the microtubule depolymerase KIF2C reduces chromosome segregation errors and mitotic-origin aneuploidy at the 2-cell stage. The number of first divisions that show alternative cleavage patterns (associated with failed embryo development in IVF clinics) are also reduced with KIF2C activation. Our findings demonstrate that modulation of microtubule dynamics is a potential therapeutic route to improving human embryo quality and IVF outcomes.

Mitosis is a complex self-organising process that achieves high fidelity separation of duplicated chromosomes into two daughter cells through capture and alignment of chromosomes to the spindle mid-plane. Chromosome movements are driven by kinetochores, multi-protein machines that attach chromosomes to microtubules (MTs), both controlling and generating directional forces. Using lattice light sheet microscopy imaging and automated near-complete tracking of kinetochores at fine spatio-temporal resolution, we produce a detailed atlas of kinetochore metaphase-anaphase dynamics in untransformed human cells (RPE1). We determined the support from this dataset for 17 models of metaphase dynamics using Bayesian inference, demonstrating (1) substantial sister asymmetry that transversely organises the metaphase plate (MPP), (2) substantial spatial organisation of KT dynamic properties within the MPP, and (3) mechanical parameter time dependence, K-fiber forces tuning over the last 5 mins of metaphase towards a set point referred to as the anaphase ready state. These spatio-temporal trends are robust to spindle assembly pathways that are error-prone, suggesting the underpinning processes of kinetochore heterogeneity are intrinsic to mitosis and possibly by design.